We now provide a proof of the Cramér-Rao limit following an approach similar to the Ref.[1]}. In order to prove this (REF ), we have to make the assumption that the estimator \(\hat{\theta }\left(x\right)\)  must be unbiased. This means that on average, the estimator \(\hat{\theta }\)  is equal to the true value of \(\theta \)  ;
\(\textbf {E}\left(\hat{\theta }\left(x \right)\right)\equiv \theta .\) 
